CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.04 - April
Issue No.04 - April (2012 vol.34)
A. Kassim , Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.
probability, computer vision, image segmentation, feedback process, active visual segmentation, human visual system, visual attention, fixation point, Cartesian space, probabilistic boundary edge map, mid-level visual cue, low-level visual cue, segmentation refinement process, Image edge detection, Image segmentation, Probabilistic logic, Visual system, Humans, Visualization, Image color analysis, visual attention., Fixation-based segmentation, object segmentation, polar space, cue integration, scale invariance
A. Kassim, "Active Visual Segmentation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 4, pp. 639-653, April 2012, doi:10.1109/TPAMI.2011.171